Relevant bibliographies by topics / Residual autocorrelation

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Residual autocorrelation'

Author: Grafiati
Published: 4 June 2021
Last updated: 2 February 2022

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Journal articles on the topic "Residual autocorrelation"

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Dash, Mihir. "Beta Estimation in Indian Stock Markets - Some Issues." Asian Journal of Finance & Accounting 7, no. 2 (2015): 23. http://dx.doi.org/10.5296/ajfa.v7i2.6751.

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<p>This study examines the reliability of the OLS beta estimates in Indian stock markets by considering the residual characteristics of the market model regressions. The statistics used include the coefficient of determination (R<sup>2</sup>), the F-test for significance of the regression coefficient, the Durbin-Watson test for serial autocorrelation, the residual autocorrelation function, the Kolmogorov-Smirnov and Shapiro-Wilk tests for normality of the residuals, the presence of outliers, and White’s test for heteroskedasticity.</p><p>The results of the study i
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Franses, Philip Hans. "Testing for residual autocorrelation in growth curve models." Technological Forecasting and Social Change 69, no. 2 (2002): 195–204. http://dx.doi.org/10.1016/s0040-1625(01)00148-2.

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Fasso, Alessandro. "Residual Autocorrelation Distribution in the Validation Data Set." Journal of Time Series Analysis 21, no. 2 (2000): 143–53. http://dx.doi.org/10.1111/1467-9892.00178.

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Brüggemann, Ralf, Helmut Lütkepohl, and Pentti Saikkonen. "Residual autocorrelation testing for vector error correction models." Journal of Econometrics 134, no. 2 (2006): 579–604. http://dx.doi.org/10.1016/j.jeconom.2005.07.006.

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Dutkowski, Gregory W., João Costa e Silva, Arthur R. Gilmour, Hubert Wellendorf, and Alexandre Aguiar. "Spatial analysis enhances modelling of a wide variety of traits in forest genetic trials." Canadian Journal of Forest Research 36, no. 7 (2006): 1851–70. http://dx.doi.org/10.1139/x06-059.

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Spatial analysis of progeny trial data improved predicted genetic responses by more than 10% for around 20 of the 216 variables tested, although, in general, the gains were more modest. The spatial method partitions the residual variance into an independent component and a two-dimensional spatially autocorrelated component and is fitted using REML. The largest improvements in likelihood were for height. Traits that exhibit little spatial structure (stem counts, form, and branching) did not respond as often. The spatial component represented up to 50% of the total residual variance, usually sub
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DEWIANTARI, NI KADEK YUNI, I. WAYAN SUMARJAYA, and G. K. GANDHIADI. "PETA KENDALI EWMA RESIDUAL PADA DATA BERAUTOKORELASI." E-Jurnal Matematika 8, no. 1 (2019): 64. http://dx.doi.org/10.24843/mtk.2019.v08.i01.p236.

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Control charts with autocorrelation can be overcome by creating control chart with residuals from the best forecasting model. EWMA control chart is a alternative to the Shewhart control chart when detecting small shifts. The purpose of this study is to make the best forecasting model to obtain residual, and see the stability of the rupiah exchange rate against US dollar using EWMA control chart with residual. The best model of the case is ARIMA (1,1,1). The results of the EWMA residual control chart with ? = 0.1 there is a pattern that makes the process unstable.
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Adams, Douglas E., and Randall J. Allemang. "Residual frequency autocorrelation as an indicator of non-linearity." International Journal of Non-Linear Mechanics 36, no. 8 (2001): 1197–211. http://dx.doi.org/10.1016/s0020-7462(00)00090-1.

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Marazzi, Alfio, and Victor J. Yohai. "Robust Box–Cox transformations based on minimum residual autocorrelation." Computational Statistics & Data Analysis 50, no. 10 (2006): 2752–68. http://dx.doi.org/10.1016/j.csda.2005.04.007.

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Bazilevsky, M. P. "Research of New Criteria for Detecting First-order Residuals Autocorrelation in Regression Models." Mathematics and Mathematical Modeling, no. 3 (August 3, 2018): 13–25. http://dx.doi.org/10.24108/mathm.0318.0000102.

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When estimating regression models using the least squares method, one of its prerequisites is the lack of autocorrelation in the regression residuals. The presence of autocorrelation in the residuals makes the least-squares regression estimates to be ineffective, and the standard errors of these estimates to be untenable. Quantitatively, autocorrelation in the residuals of the regression model has traditionally been estimated using the Durbin-Watson statistic, which is the ratio of the sum of the squares of differences of consecutive residual values to the sum of squares of the residuals. Unfo
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Bennis, Saad, and Pierre Bruneau. "Comparaison de méthodes d'estimation des débits journaliers." Canadian Journal of Civil Engineering 20, no. 3 (1993): 480–89. http://dx.doi.org/10.1139/l93-062.

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The aim of the first part of our research, described in this paper, was to compare daily streamflow estimation techniques and models. A general application software named DebEst was developed for the purpose. The Saint-François River basin was used as a physical test area because of the availability of several hydrometric stations in this region. All techniques and models used gave good results. However, principal component analysis and multiple regression applied to deterministic models gave better results than ARIMA models. The least square recursive algorithm was more flexible than the othe
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Dissertations / Theses on the topic "Residual autocorrelation"

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Miralha, Lorrayne. "ACCOUNTING FOR SPATIAL AUTOCORRELATION IN MODELING THE DISTRIBUTION OF WATER QUALITY VARIABLES." UKnowledge, 2018. https://uknowledge.uky.edu/geography_etds/55.

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Several studies in hydrology have reported differences in outcomes between models in which spatial autocorrelation (SAC) is accounted for and those in which SAC is not. However, the capacity to predict the magnitude of such differences is still ambiguous. In this thesis, I hypothesized that SAC, inherently possessed by a response variable, influences spatial modeling outcomes. I selected ten watersheds in the USA and analyzed them to determine whether water quality variables with higher Moran’s I values undergo greater increases in the coefficient of determination (R²) and greater decreases in
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Guarnieri, Jean Paulo. "EFICIÊNCIA DOS GRÁFICOS DE CONTROLE NA DETECÇÃO DE OUTLIERS EM PROCESSOS AUTORREGRESSIVOS E DE MÉDIAS MÓVEIS." Universidade Federal de Santa Maria, 2010. http://repositorio.ufsm.br/handle/1/8171.

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This research approaches the prediction models application along with the usage of residual control charts to evaluate productive processes with characteristics of autocorrelation in its samples. The overall objective was to determine the Individual Measurement Control Charts (IMCC) and the Exponentially Weighted Moving Average (EWMA) efficiency when applied to residuals of ARIMA class, to the outliers detection in autocorrelated processes, as well as identifying the autocorrelation influence and the amplitude of the outlier concerning the charts detection capacity. To each AR(1) and MA(1), 6
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Cervantes, Juan. "Tempering spatial autocorrelation in the residuals of linear and generalized models by incorporating selected eigenvectors." Diss., University of Iowa, 2018. https://ir.uiowa.edu/etd/6388.

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In order to account for spatial correlation in residuals in regression models for areal and lattice data, different disciplines have developed distinct approaches. Bayesian spatial statistics typically has used a Gaussian conditional autoregressive (CAR) prior on random effects, while geographers utilize Moran's I statistic as a measure of spatial autocorrelation and the basis for creating spatial models. Recent work in both fields has recognized and built on a common feature of the two approaches, specifically the implicit or explicit incorporation into the linear predictor of eigenvectors of
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Frühwirth-Schnatter, Sylvia. "Recursive Residuals and Model Diagnostics for Normal and Non-Normal State Space Models." Department of Statistics and Mathematics, WU Vienna University of Economics and Business, 1994. http://epub.wu.ac.at/1540/1/document.pdf.

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Model diagnostics for normal and non-normal state space models is based on recursive residuals which are defined from the one-step ahead predictive distribution. Routine calculation of these residuals is discussed in detail. Various tools of diagnostics are suggested to check e.g. for wrong observation distributions and for autocorrelation. The paper also covers such topics as model diagnostics for discrete time series, model diagnostics for generalized linear models, and model discrimination via Bayes factors. (author's abstract)<br>Series: Forschungsberichte / Institut für Statistik
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Esstafa, Youssef. "Modèles de séries temporelles à mémoire longue avec innovations dépendantes." Thesis, Bourgogne Franche-Comté, 2019. http://www.theses.fr/2019UBFCD021.

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Dans cette thèse nous considérons, dans un premier temps, le problème de l'analyse statistique des modèles FARIMA (Fractionally AutoRegressive Integrated Moving-Average) induits par un bruit blanc non corrélé mais qui peut contenir des dépendances non linéaires très générales. Ces modèles sont appelés FARIMA faibles et permettent de modéliser des processus à mémoire longue présentant des dynamiques non linéaires, de structures souvent non-identifiées, très générales. Relâcher l'hypothèse d'indépendance sur le terme d'erreur, une hypothèse habituellement imposée dans la littérature, permet aux
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Liou, Chu Pheuil. "Tests d'ajustement reposant sur les méthodes d'ondelettes dans les modèles ARMA avec un terme d'erreur qui est une différence de martingales conditionnellement hétéroscédastique." Thèse, 2019. http://hdl.handle.net/1866/22551.

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Ursu, Eugen. "Contributions dans l'analyse des modèles vectoriels de séries chronologiques saisonnières et périodiques." Thèse, 2009. http://hdl.handle.net/1866/6539.

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Books on the topic "Residual autocorrelation"

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McCleary, Richard, David McDowall, and Bradley J. Bartos. Noise Modeling. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190661557.003.0003.

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Chapter 3 introduces the Box-Jenkins AutoRegressive Integrated Moving Average (ARIMA) noise modeling strategy. The strategy begins with a test of the Normality assumption using a Kolomogov-Smirnov (KS) statistic. Non-Normal time series are transformed with a Box-Cox procedure is applied. A tentative ARIMA noise model is then identified from a sample AutoCorrelation function (ACF). If the sample ACF identifies a nonstationary model, the time series is differenced. Integer orders p and q of the underlying autoregressive and moving average structures are then identified from the ACF and partial a
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Book chapters on the topic "Residual autocorrelation"

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Bauwens, L., and A. Rasquero. "Approximate HPD Regions for Testing Residual Autocorrelation Using Augmented Regressions." In Computer Intensive Methods in Statistics. Physica-Verlag HD, 1993. http://dx.doi.org/10.1007/978-3-642-52468-4_3.

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Ramos, Patrícia Ferreira, Manuel Cabral Morais, António Pacheco, and Wolfgang Schmid. "Assessing the Impact of Autocorrelation in Misleading Signals in Simultaneous Residual Schemes for the Process Mean and Variance: A Stochastic Ordering Approach." In Frontiers in Statistical Quality Control 10. Physica-Verlag HD, 2012. http://dx.doi.org/10.1007/978-3-7908-2846-7_3.

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"Autocorrelation of residuals – 1." In Advanced Econometric Theory. Routledge, 2013. http://dx.doi.org/10.4324/9780203180754-11.

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"Autocorrelation of residuals – 2." In Advanced Econometric Theory. Routledge, 2013. http://dx.doi.org/10.4324/9780203180754-12.

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Leitgöb, Heinz, Daniel Seddig, Peter Schmidt, Edward Sosu, and Eldad Davidov. "Longitudinal Measurement (Non)Invariance in Latent Constructs." In Measurement Error in Longitudinal Data. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780198859987.003.0010.

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The chapter discusses the basic principles and core problems of latent variable panel modelling, with a focus on the specification of error structures and (the evaluation of) longitudinal measurement invariance. We address alternative specifications of autocorrelative error structures, and demonstrate how to decompose the indicators’ residual variances into indicator-specific and random error components. Furthermore, besides describing the conventional global testing strategy for measurement (non)invariance, we contribute to the literature by integrating theoretical and analytical elements not yet extensively discussed outside the respective disciplines. We (i) introduce response shift theory as viable theoretical basis for the occurrence of noninvariance across time; (ii) provide a detailed description of model and scale identification strategies, accompanied by a critical reflection of their potential to adequately detect noninvariant parameters; and (iii) discuss the concepts of partial and approximate measurement invariance as well as the decomposition of response shifts and true change as different strategies of how to deal with measurement noninvariance.
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Conference papers on the topic "Residual autocorrelation"

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Schwall, Matthew L., and J. Christian Gerdes. "Residual Autocorrelation in Probabilistic Model-Based Diagnostics." In ASME 2005 International Mechanical Engineering Congress and Exposition. ASMEDC, 2005. http://dx.doi.org/10.1115/imece2005-82165.

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The performance of model-based diagnostic techniques depends not only on the quality of the residuals generated using the models, but also on the method used to interpret the residuals. Robust residuals can often be interpreted deterministically, but noisy residuals can benefit from being interpreted probabilistically. A probabilistic framework enables the modeling of uncertainty and the relationship between multiple faults and multiple residuals. However, it is not well-suited for representing residual dynamics, and as a result, residuals must be assumed to not be autocorrelated. Since this c
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Chenyu, Ding, Yue Ruihua, and Li Yuandong. "ARL Study of Second Order Autocorrelation Residual Control Chart and its Application in MAP." In 2018 37th Chinese Control Conference (CCC). IEEE, 2018. http://dx.doi.org/10.23919/chicc.2018.8483040.

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Hamzah, N. H., S. Yaacob, H. Muthusamy, and N. Hamzah. "Analysis of the residual between the model and the data using autocorrelation function for satellite attitude estimation." In 2013 IEEE 9th International Colloquium on Signal Processing & its Applications (CSPA). IEEE, 2013. http://dx.doi.org/10.1109/cspa.2013.6530018.

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Jonas, M. "The Application of the Time Series Theory to Processing Data From the SBAS Receiver in Safety Mode." In 2012 Joint Rail Conference. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/jrc2012-74033.

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Before satellite-based augmentation systems (SBAS) such as the Wide Area Augmentation System (WAAS) in the USA, and the European Geostationary Navigation Overlay Service (EGNOS), will be used in railway safety-related applications, it is necessary to determine reliability attributes of these systems as quality measures from the user’s point of view. It is necessary to find new methods of processing data from the SBAS system in accordance with strict railway standards. For this purposes data from the SBAS receiver with the Safety of Life Service was processed by means of the time series theory.
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Miceli, P. A., and W. Dale Blair. "Note on Autocorrelation of the Residuals of the NCV Kalman Filter Tracking a Maneuvering Target." In 2020 IEEE Radar Conference (RadarConf20). IEEE, 2020. http://dx.doi.org/10.1109/radarconf2043947.2020.9266472.

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